Insulated Gate Bipolar Transistors (IGBTs) are currently the most widely used active switching devices in power electronics converters. Therefore, a variety of researches have been conducted on the modeling of IGBT. The current IGBT models are mainly divided into two categories from the users' point of view, namely analytical models and behavioral models.
Analytical models are established mainly based on the principles of semiconductor physics, according to the internal structure and the carrier transport of the IGBT. The analytical models can simulate both the steady-state and transient characteristics of IGBTs accurately. The conventional analytical models include Hefner model, Kuang Sheng model, Kraus model, etc. Although the analytical models are accurate, the models suffer for complicated circuit simulations due to complex structures, difficult parameter extractions, large amount of computation and difficulties in convergence.
Behavioral models ignore some internal physical mechanisms of IGBTs, and are more focused on fitting the external characteristics of the devices. The behavioral models can be applied in circuit simulations and can be more accurate than the ideal switch model. As compared to the analytical models, behavioral models are simpler but less accurate, and are less adaptable to different working conditions. Parameter extraction of behavioral models is still complicated, since the parameters have to be extracted from experiment results.